Urban Land Classification Through The Analysis of Satellite and Aerial Hyperspectral Data in Tuscany Region (TUS:CAN Project)

The paper presents the activities of the TUS:CAN project, funded by ASI, the Italian Space Agency, and focuses on the experimental results obtained at the end of the first year. A Google Earth Engine implementation has been designed for spaceborne image collection and handling, spatial resolution enhancement, classification, and cross-validation with airborne hyperspectral image data and in situ spectral signature acquisitions. Initial trials using Sentinel-2 multispectral imagery over a 95 km 2 area validate the reliability and consistency of a technique to categorize and quantitatively assess natural and man-made land covers, aimed at updating maps and promoting sustainable land use.